{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "gUMZYLdp222_"
      ],
      "toc_visible": true,
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# build Darknet"
      ],
      "metadata": {
        "id": "gUMZYLdp222_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4HBBzpMy0W8",
        "outputId": "f6283184-e87b-40b2-f83e-fe1a1c0703d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "build-essential is already the newest version (12.9ubuntu3).\n",
            "cmake is already the newest version (3.22.1-1ubuntu1.22.04.2).\n",
            "git is already the newest version (1:2.34.1-1ubuntu1.11).\n",
            "libopencv-dev is already the newest version (4.5.4+dfsg-9ubuntu4+jammy0).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n",
            "/root/src\n",
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 17896, done.\u001b[K\n",
            "remote: Counting objects: 100% (2368/2368), done.\u001b[K\n",
            "remote: Compressing objects: 100% (735/735), done.\u001b[K\n",
            "remote: Total 17896 (delta 1741), reused 2236 (delta 1624), pack-reused 15528\u001b[K\n",
            "Receiving objects: 100% (17896/17896), 17.98 MiB | 7.47 MiB/s, done.\n",
            "Resolving deltas: 100% (12213/12213), done.\n",
            "/root/src/darknet/build\n",
            "-- Darknet v2.0-225-g277ed9f4\n",
            "-- The C compiler identification is GNU 11.4.0\n",
            "-- The CXX compiler identification is GNU 11.4.0\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Check for working C compiler: /usr/bin/cc - skipped\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- Looking for a CUDA compiler\n",
            "-- Looking for a CUDA compiler - /usr/local/cuda/bin/nvcc\n",
            "-- CUDA detected. Darknet will use the GPU.\n",
            "-- The CUDA compiler identification is NVIDIA 12.2.140\n",
            "-- Detecting CUDA compiler ABI info\n",
            "-- Detecting CUDA compiler ABI info - done\n",
            "-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped\n",
            "-- Detecting CUDA compile features\n",
            "-- Detecting CUDA compile features - done\n",
            "-- Found CUDAToolkit: /usr/local/cuda/include (found version \"12.2.140\") \n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "-- Found Threads: TRUE  \n",
            "-- Found cuDNN library: /usr/lib/x86_64-linux-gnu/libcudnn.so\n",
            "-- Found cuDNN include: /usr/include\n",
            "-- Hardware is 32-bit or 64-bit, and seems to be Intel or AMD:  x86_64\n",
            "-- Found Threads \n",
            "-- Found OpenCV: /usr (found version \"4.5.4\") \n",
            "-- Found OpenCV 4.5.4\n",
            "-- Found OpenMP \n",
            "-- Enabling AVX and SSE optimizations.\n",
            "-- Making an optimized release build.\n",
            "-- Skipping Doxygen (not found)\n",
            "-- Setting up DARKNET OBJ\n",
            "-- Setting up DARKNET LIB\n",
            "-- Setting up DARKNET CLI\n",
            "-- Configuring done (14.3s)\n",
            "-- Generating done (0.0s)\n",
            "-- Build files have been written to: /root/src/darknet/build\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -S/root/src/darknet -B/root/src/darknet/build --check-build-system CMakeFiles/Makefile.cmake 0\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_progress_start /root/src/darknet/build/CMakeFiles /root/src/darknet/build//CMakeFiles/progress.marks\n",
            "make  -f CMakeFiles/Makefile2 all\n",
            "make[1]: Entering directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknetobjlib.dir/build.make src-lib/CMakeFiles/darknetobjlib.dir/depend\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-lib /root/src/darknet/build /root/src/darknet/build/src-lib /root/src/darknet/build/src-lib/CMakeFiles/darknetobjlib.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknetobjlib.dir/build.make src-lib/CMakeFiles/darknetobjlib.dir/build\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "[  1%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/Timing.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/Timing.cpp.o -MF CMakeFiles/darknetobjlib.dir/Timing.cpp.o.d -o CMakeFiles/darknetobjlib.dir/Timing.cpp.o -c /root/src/darknet/src-lib/Timing.cpp\n",
            "[  2%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/Chart.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/Chart.cpp.o -MF CMakeFiles/darknetobjlib.dir/Chart.cpp.o.d -o CMakeFiles/darknetobjlib.dir/Chart.cpp.o -c /root/src/darknet/src-lib/Chart.cpp\n",
            "[  3%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/activation_kernels.cu -o CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o\n",
            "[  4%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o -c /root/src/darknet/src-lib/activation_layer.cpp\n",
            "[  5%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/activations.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/activations.cpp.o -MF CMakeFiles/darknetobjlib.dir/activations.cpp.o.d -o CMakeFiles/darknetobjlib.dir/activations.cpp.o -c /root/src/darknet/src-lib/activations.cpp\n",
            "[  6%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/art.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/art.cpp.o -MF CMakeFiles/darknetobjlib.dir/art.cpp.o.d -o CMakeFiles/darknetobjlib.dir/art.cpp.o -c /root/src/darknet/src-lib/art.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/activations.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat gradient(float, ACTIVATION)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/activations.cpp:362:44:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis statement may fall through [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wimplicit-fallthrough=\u0007-Wimplicit-fallthrough=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  362 |                         \u001b[01;35m\u001b[Kdarknet_fatal_error(DARKNET_LOC, \"should be used custom NORM_CHAN or NORM_CHAN_SOFTMAX-function for gradient\")\u001b[m\u001b[K;\n",
            "      |                         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/activations.cpp:363:17:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Khere\n",
            "  363 |                 \u001b[01;36m\u001b[Kcase\u001b[m\u001b[K ELU:\n",
            "      |                 \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "[  8%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o -c /root/src/darknet/src-lib/avgpool_layer.cpp\n",
            "[  9%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/avgpool_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o\n",
            "[ 10%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o -c /root/src/darknet/src-lib/batchnorm_layer.cpp\n",
            "[ 11%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/blas.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/blas.cpp.o -MF CMakeFiles/darknetobjlib.dir/blas.cpp.o.d -o CMakeFiles/darknetobjlib.dir/blas.cpp.o -c /root/src/darknet/src-lib/blas.cpp\n",
            "[ 12%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/blas_kernels.cu -o CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o\n",
            "[ 13%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/box.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/box.cpp.o -MF CMakeFiles/darknetobjlib.dir/box.cpp.o.d -o CMakeFiles/darknetobjlib.dir/box.cpp.o -c /root/src/darknet/src-lib/box.cpp\n",
            "[ 15%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/captcha.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/captcha.cpp.o -MF CMakeFiles/darknetobjlib.dir/captcha.cpp.o.d -o CMakeFiles/darknetobjlib.dir/captcha.cpp.o -c /root/src/darknet/src-lib/captcha.cpp\n",
            "[ 16%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/cifar.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/cifar.cpp.o -MF CMakeFiles/darknetobjlib.dir/cifar.cpp.o.d -o CMakeFiles/darknetobjlib.dir/cifar.cpp.o -c /root/src/darknet/src-lib/cifar.cpp\n",
            "[ 17%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/classifier.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/classifier.cpp.o -MF CMakeFiles/darknetobjlib.dir/classifier.cpp.o.d -o CMakeFiles/darknetobjlib.dir/classifier.cpp.o -c /root/src/darknet/src-lib/classifier.cpp\n",
            "[ 18%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/coco.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/coco.cpp.o -MF CMakeFiles/darknetobjlib.dir/coco.cpp.o.d -o CMakeFiles/darknetobjlib.dir/coco.cpp.o -c /root/src/darknet/src-lib/coco.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/classifier.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid train_classifier(char*, char*, char*, int*, int, int, int, int, int, int, char*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/classifier.cpp:217:72:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%lld\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Klong long int\u001b[m\u001b[K’, but argument 8 has type ‘\u001b[01m\u001b[Kuint64_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  217 |                 printf(\"%d, %.3f: %f, %f avg, %f rate, %lf seconds, \u001b[01;35m\u001b[K%lld\u001b[m\u001b[K images, %f hours left\\n\", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), \u001b[32m\u001b[K*net.seen\u001b[m\u001b[K, avg_time);\n",
            "      |                                                                     \u001b[01;35m\u001b[K~~~^\u001b[m\u001b[K                                                                                                                                                   \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n",
            "      |                                                                        \u001b[01;35m\u001b[K|\u001b[m\u001b[K                                                                                                                                                   \u001b[32m\u001b[K|\u001b[m\u001b[K\n",
            "      |                                                                        \u001b[01;35m\u001b[Klong long int\u001b[m\u001b[K                                                                                                                                       \u001b[32m\u001b[Kuint64_t {aka long unsigned int}\u001b[m\u001b[K\n",
            "      |                                                                     \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "[ 19%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/col2im.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/col2im.cpp.o -MF CMakeFiles/darknetobjlib.dir/col2im.cpp.o.d -o CMakeFiles/darknetobjlib.dir/col2im.cpp.o -c /root/src/darknet/src-lib/col2im.cpp\n",
            "[ 20%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/col2im_kernels.cu -o CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o\n",
            "[ 22%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/compare.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/compare.cpp.o -MF CMakeFiles/darknetobjlib.dir/compare.cpp.o.d -o CMakeFiles/darknetobjlib.dir/compare.cpp.o -c /root/src/darknet/src-lib/compare.cpp\n",
            "[ 23%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o -c /root/src/darknet/src-lib/connected_layer.cpp\n",
            "[ 24%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o -c /root/src/darknet/src-lib/conv_lstm_layer.cpp\n",
            "[ 25%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/convolutional_kernels.cu -o CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o\n",
            "[ 26%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o -c /root/src/darknet/src-lib/convolutional_layer.cpp\n",
            "[ 27%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o -c /root/src/darknet/src-lib/cost_layer.cpp\n",
            "[ 29%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o -c /root/src/darknet/src-lib/crnn_layer.cpp\n",
            "[ 30%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o -c /root/src/darknet/src-lib/crop_layer.cpp\n",
            "[ 31%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/crop_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o\n",
            "[ 32%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o -MF CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o.d -o CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o -c /root/src/darknet/src-lib/dark_cuda.cpp\n",
            "[ 33%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o -c /root/src/darknet/src-lib/darknet_args_and_parms.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid pre_allocate_pinned_memory(size_t)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:444:47:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K \u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  444 |                         printf(\" Allocated %ll\u001b[01;35m\u001b[K \u001b[m\u001b[Kpinned block \\n\", pinned_block_size);\n",
            "      |                                               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:444:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  444 |                         printf(\u001b[01;35m\u001b[K\" Allocated %ll pinned block \\n\"\u001b[m\u001b[K, pinned_block_size);\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat* cuda_make_array_pinned_preallocated(float*, size_t)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:466:57:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K,\u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  466 |                         printf(\"\\n Pinned block_id = %ll\u001b[01;35m\u001b[K,\u001b[m\u001b[K filled = %f %% \\n\", pinned_block_id, filled);\n",
            "      |                                                         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:466:69:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%f\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kdouble\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  466 |                         printf(\"\\n Pinned block_id = %ll, filled = \u001b[01;35m\u001b[K%f\u001b[m\u001b[K %% \\n\", \u001b[32m\u001b[Kpinned_block_id\u001b[m\u001b[K, filled);\n",
            "      |                                                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "      |                                                                     \u001b[01;35m\u001b[K|\u001b[m\u001b[K         \u001b[32m\u001b[K|\u001b[m\u001b[K\n",
            "      |                                                                     \u001b[01;35m\u001b[Kdouble\u001b[m\u001b[K    \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n",
            "      |                                                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:466:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  466 |                         printf(\u001b[01;35m\u001b[K\"\\n Pinned block_id = %ll, filled = %f %% \\n\"\u001b[m\u001b[K, pinned_block_id, filled);\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:481:78:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K \u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  481 |                         printf(\"Try to allocate new pinned memory, size = %ll\u001b[01;35m\u001b[K \u001b[m\u001b[KMB \\n\", size / (1024 * 1024));\n",
            "      |                                                                              \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:481:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  481 |                         printf(\u001b[01;35m\u001b[K\"Try to allocate new pinned memory, size = %ll MB \\n\"\u001b[m\u001b[K, size / (1024 * 1024));\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:487:77:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K \u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  487 |                         printf(\"Try to allocate new pinned BLOCK, size = %ll\u001b[01;35m\u001b[K \u001b[m\u001b[KMB \\n\", size / (1024 * 1024));\n",
            "      |                                                                             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:487:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  487 |                         printf(\u001b[01;35m\u001b[K\"Try to allocate new pinned BLOCK, size = %ll MB \\n\"\u001b[m\u001b[K, size / (1024 * 1024));\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "[ 34%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o -c /root/src/darknet/src-lib/darknet_cfg_and_state.cpp\n",
            "[ 36%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o -c /root/src/darknet/src-lib/darknet_format_and_colour.cpp\n",
            "[ 37%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o -c /root/src/darknet/src-lib/darknet_utils.cpp\n",
            "[ 38%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/data.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/data.cpp.o -MF CMakeFiles/darknetobjlib.dir/data.cpp.o.d -o CMakeFiles/darknetobjlib.dir/data.cpp.o -c /root/src/darknet/src-lib/data.cpp\n",
            "[ 39%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/demo.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/demo.cpp.o -MF CMakeFiles/darknetobjlib.dir/demo.cpp.o.d -o CMakeFiles/darknetobjlib.dir/demo.cpp.o -c /root/src/darknet/src-lib/demo.cpp\n",
            "[ 40%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o -c /root/src/darknet/src-lib/detection_layer.cpp\n",
            "[ 41%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/detector.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/detector.cpp.o -MF CMakeFiles/darknetobjlib.dir/detector.cpp.o.d -o CMakeFiles/darknetobjlib.dir/detector.cpp.o -c /root/src/darknet/src-lib/detector.cpp\n",
            "[ 43%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/dice.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dice.cpp.o -MF CMakeFiles/darknetobjlib.dir/dice.cpp.o.d -o CMakeFiles/darknetobjlib.dir/dice.cpp.o -c /root/src/darknet/src-lib/dice.cpp\n",
            "[ 44%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o -c /root/src/darknet/src-lib/dropout_layer.cpp\n",
            "[ 45%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/dropout_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o\n",
            "[ 46%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o -c /root/src/darknet/src-lib/gaussian_yolo_layer.cpp\n",
            "[ 47%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/gemm.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/gemm.cpp.o -MF CMakeFiles/darknetobjlib.dir/gemm.cpp.o.d -o CMakeFiles/darknetobjlib.dir/gemm.cpp.o -c /root/src/darknet/src-lib/gemm.cpp\n",
            "[ 48%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/go.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/go.cpp.o -MF CMakeFiles/darknetobjlib.dir/go.cpp.o.d -o CMakeFiles/darknetobjlib.dir/go.cpp.o -c /root/src/darknet/src-lib/go.cpp\n",
            "[ 50%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o -c /root/src/darknet/src-lib/gru_layer.cpp\n",
            "[ 51%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/http_stream.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/http_stream.cpp.o -MF CMakeFiles/darknetobjlib.dir/http_stream.cpp.o.d -o CMakeFiles/darknetobjlib.dir/http_stream.cpp.o -c /root/src/darknet/src-lib/http_stream.cpp\n",
            "[ 52%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/im2col.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/im2col.cpp.o -MF CMakeFiles/darknetobjlib.dir/im2col.cpp.o.d -o CMakeFiles/darknetobjlib.dir/im2col.cpp.o -c /root/src/darknet/src-lib/im2col.cpp\n",
            "[ 53%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/im2col_kernels.cu -o CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o\n",
            "[ 54%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/image.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/image.cpp.o -MF CMakeFiles/darknetobjlib.dir/image.cpp.o.d -o CMakeFiles/darknetobjlib.dir/image.cpp.o -c /root/src/darknet/src-lib/image.cpp\n",
            "[ 55%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o -MF CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o.d -o CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o -c /root/src/darknet/src-lib/image_opencv.cpp\n",
            "[ 56%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/layer.cpp.o -c /root/src/darknet/src-lib/layer.cpp\n",
            "[ 58%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/list.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/list.cpp.o -MF CMakeFiles/darknetobjlib.dir/list.cpp.o.d -o CMakeFiles/darknetobjlib.dir/list.cpp.o -c /root/src/darknet/src-lib/list.cpp\n",
            "[ 59%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/local_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/local_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/local_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/local_layer.cpp.o -c /root/src/darknet/src-lib/local_layer.cpp\n",
            "[ 60%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o -c /root/src/darknet/src-lib/lstm_layer.cpp\n",
            "[ 61%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/matrix.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/matrix.cpp.o -MF CMakeFiles/darknetobjlib.dir/matrix.cpp.o.d -o CMakeFiles/darknetobjlib.dir/matrix.cpp.o -c /root/src/darknet/src-lib/matrix.cpp\n",
            "[ 62%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o -c /root/src/darknet/src-lib/maxpool_layer.cpp\n",
            "[ 63%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/maxpool_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o\n",
            "[ 65%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/network.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/network.cpp.o -MF CMakeFiles/darknetobjlib.dir/network.cpp.o.d -o CMakeFiles/darknetobjlib.dir/network.cpp.o -c /root/src/darknet/src-lib/network.cpp\n",
            "[ 66%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/network_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/network_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/network_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/network_kernels.cu -o CMakeFiles/darknetobjlib.dir/network_kernels.cu.o\n",
            "[ 67%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/nightmare.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/nightmare.cpp.o -MF CMakeFiles/darknetobjlib.dir/nightmare.cpp.o.d -o CMakeFiles/darknetobjlib.dir/nightmare.cpp.o -c /root/src/darknet/src-lib/nightmare.cpp\n",
            "[ 68%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o -c /root/src/darknet/src-lib/normalization_layer.cpp\n",
            "[ 69%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/option_list.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/option_list.cpp.o -MF CMakeFiles/darknetobjlib.dir/option_list.cpp.o.d -o CMakeFiles/darknetobjlib.dir/option_list.cpp.o -c /root/src/darknet/src-lib/option_list.cpp\n",
            "[ 70%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/parser.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/parser.cpp.o -MF CMakeFiles/darknetobjlib.dir/parser.cpp.o.d -o CMakeFiles/darknetobjlib.dir/parser.cpp.o -c /root/src/darknet/src-lib/parser.cpp\n",
            "[ 72%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/region_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/region_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/region_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/region_layer.cpp.o -c /root/src/darknet/src-lib/region_layer.cpp\n",
            "[ 73%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o -c /root/src/darknet/src-lib/reorg_layer.cpp\n",
            "[ 74%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o -c /root/src/darknet/src-lib/reorg_old_layer.cpp\n",
            "[ 75%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o -c /root/src/darknet/src-lib/representation_layer.cpp\n",
            "[ 76%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/rnn.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/rnn.cpp.o -MF CMakeFiles/darknetobjlib.dir/rnn.cpp.o.d -o CMakeFiles/darknetobjlib.dir/rnn.cpp.o -c /root/src/darknet/src-lib/rnn.cpp\n",
            "[ 77%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o -c /root/src/darknet/src-lib/rnn_layer.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/rnn.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[K{anonymous}::float_pair get_rnn_data(unsigned char*, size_t*, int, size_t, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/rnn.cpp:107:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison is always false due to limited range of data type [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wtype-limits\u0007-Wtype-limits\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  107 |                         if(\u001b[01;35m\u001b[Kcurr > 255\u001b[m\u001b[K || curr <= 0 || next > 255 || next <= 0)\n",
            "      |                            \u001b[01;35m\u001b[K~~~~~^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/rnn.cpp:107:60:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison is always false due to limited range of data type [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wtype-limits\u0007-Wtype-limits\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  107 |                         if(curr > 255 || curr <= 0 || \u001b[01;35m\u001b[Knext > 255\u001b[m\u001b[K || next <= 0)\n",
            "      |                                                       \u001b[01;35m\u001b[K~~~~~^~~~~\u001b[m\u001b[K\n",
            "[ 79%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o -MF CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o.d -o CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o -c /root/src/darknet/src-lib/rnn_vid.cpp\n",
            "[ 80%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/route_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/route_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/route_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/route_layer.cpp.o -c /root/src/darknet/src-lib/route_layer.cpp\n",
            "[ 81%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o -c /root/src/darknet/src-lib/sam_layer.cpp\n",
            "[ 82%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o -c /root/src/darknet/src-lib/scale_channels_layer.cpp\n",
            "[ 83%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o -c /root/src/darknet/src-lib/shortcut_layer.cpp\n",
            "[ 84%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o -c /root/src/darknet/src-lib/softmax_layer.cpp\n",
            "[ 86%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/super.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/super.cpp.o -MF CMakeFiles/darknetobjlib.dir/super.cpp.o.d -o CMakeFiles/darknetobjlib.dir/super.cpp.o -c /root/src/darknet/src-lib/super.cpp\n",
            "[ 87%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/tag.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/tag.cpp.o -MF CMakeFiles/darknetobjlib.dir/tag.cpp.o.d -o CMakeFiles/darknetobjlib.dir/tag.cpp.o -c /root/src/darknet/src-lib/tag.cpp\n",
            "[ 88%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/tree.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/tree.cpp.o -MF CMakeFiles/darknetobjlib.dir/tree.cpp.o.d -o CMakeFiles/darknetobjlib.dir/tree.cpp.o -c /root/src/darknet/src-lib/tree.cpp\n",
            "[ 89%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o -c /root/src/darknet/src-lib/upsample_layer.cpp\n",
            "[ 90%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/utils.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/utils.cpp.o -MF CMakeFiles/darknetobjlib.dir/utils.cpp.o.d -o CMakeFiles/darknetobjlib.dir/utils.cpp.o -c /root/src/darknet/src-lib/utils.cpp\n",
            "[ 91%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/voxel.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/voxel.cpp.o -MF CMakeFiles/darknetobjlib.dir/voxel.cpp.o.d -o CMakeFiles/darknetobjlib.dir/voxel.cpp.o -c /root/src/darknet/src-lib/voxel.cpp\n",
            "[ 93%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/writing.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/writing.cpp.o -MF CMakeFiles/darknetobjlib.dir/writing.cpp.o.d -o CMakeFiles/darknetobjlib.dir/writing.cpp.o -c /root/src/darknet/src-lib/writing.cpp\n",
            "[ 94%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/yolo.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/yolo.cpp.o -MF CMakeFiles/darknetobjlib.dir/yolo.cpp.o.d -o CMakeFiles/darknetobjlib.dir/yolo.cpp.o -c /root/src/darknet/src-lib/yolo.cpp\n",
            "[ 95%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o -c /root/src/darknet/src-lib/yolo_layer.cpp\n",
            "[ 96%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o -MF CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o.d -o CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o -c /root/src/darknet/src-lib/yolo_v2_class.cpp\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[ 96%] Built target darknetobjlib\n",
            "make  -f src-lib/CMakeFiles/darknet.dir/build.make src-lib/CMakeFiles/darknet.dir/depend\n",
            "make  -f src-cli/CMakeFiles/darknetcli.dir/build.make src-cli/CMakeFiles/darknetcli.dir/depend\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-lib /root/src/darknet/build /root/src/darknet/build/src-lib /root/src/darknet/build/src-lib/CMakeFiles/darknet.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-cli /root/src/darknet/build /root/src/darknet/build/src-cli /root/src/darknet/build/src-cli/CMakeFiles/darknetcli.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-cli/CMakeFiles/darknetcli.dir/build.make src-cli/CMakeFiles/darknetcli.dir/build\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknet.dir/build.make src-lib/CMakeFiles/darknet.dir/build\n",
            "[ 97%] \u001b[32mBuilding CXX object src-cli/CMakeFiles/darknetcli.dir/darknet.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-cli && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -Ddarknetcli_EXPORTS -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIE -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -MD -MT src-cli/CMakeFiles/darknetcli.dir/darknet.cpp.o -MF CMakeFiles/darknetcli.dir/darknet.cpp.o.d -o CMakeFiles/darknetcli.dir/darknet.cpp.o -c /root/src/darknet/src-cli/darknet.cpp\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "[ 98%] \u001b[32m\u001b[1mLinking CXX shared library libdarknet.so\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_link_script CMakeFiles/darknet.dir/link.txt --verbose=1\n",
            "/usr/bin/c++ -fPIC -O3 -DNDEBUG -flto=auto -fno-fat-lto-objects -shared -Wl,-soname,libdarknet.so -o libdarknet.so CMakeFiles/darknetobjlib.dir/Chart.cpp.o CMakeFiles/darknetobjlib.dir/Timing.cpp.o CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o CMakeFiles/darknetobjlib.dir/activations.cpp.o CMakeFiles/darknetobjlib.dir/art.cpp.o CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o CMakeFiles/darknetobjlib.dir/blas.cpp.o CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o CMakeFiles/darknetobjlib.dir/box.cpp.o CMakeFiles/darknetobjlib.dir/captcha.cpp.o CMakeFiles/darknetobjlib.dir/cifar.cpp.o CMakeFiles/darknetobjlib.dir/classifier.cpp.o CMakeFiles/darknetobjlib.dir/coco.cpp.o CMakeFiles/darknetobjlib.dir/col2im.cpp.o CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o CMakeFiles/darknetobjlib.dir/compare.cpp.o CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o CMakeFiles/darknetobjlib.dir/data.cpp.o CMakeFiles/darknetobjlib.dir/demo.cpp.o CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o CMakeFiles/darknetobjlib.dir/detector.cpp.o CMakeFiles/darknetobjlib.dir/dice.cpp.o CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o CMakeFiles/darknetobjlib.dir/gemm.cpp.o CMakeFiles/darknetobjlib.dir/go.cpp.o CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o CMakeFiles/darknetobjlib.dir/http_stream.cpp.o CMakeFiles/darknetobjlib.dir/im2col.cpp.o CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o CMakeFiles/darknetobjlib.dir/image.cpp.o CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o CMakeFiles/darknetobjlib.dir/layer.cpp.o CMakeFiles/darknetobjlib.dir/list.cpp.o CMakeFiles/darknetobjlib.dir/local_layer.cpp.o CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o CMakeFiles/darknetobjlib.dir/matrix.cpp.o CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o CMakeFiles/darknetobjlib.dir/network.cpp.o CMakeFiles/darknetobjlib.dir/network_kernels.cu.o CMakeFiles/darknetobjlib.dir/nightmare.cpp.o CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o CMakeFiles/darknetobjlib.dir/option_list.cpp.o CMakeFiles/darknetobjlib.dir/parser.cpp.o CMakeFiles/darknetobjlib.dir/region_layer.cpp.o CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o CMakeFiles/darknetobjlib.dir/rnn.cpp.o CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o CMakeFiles/darknetobjlib.dir/route_layer.cpp.o CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o CMakeFiles/darknetobjlib.dir/super.cpp.o CMakeFiles/darknetobjlib.dir/tag.cpp.o CMakeFiles/darknetobjlib.dir/tree.cpp.o CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o CMakeFiles/darknetobjlib.dir/utils.cpp.o CMakeFiles/darknetobjlib.dir/voxel.cpp.o CMakeFiles/darknetobjlib.dir/writing.cpp.o CMakeFiles/darknetobjlib.dir/yolo.cpp.o CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o   -L/usr/local/cuda/targets/x86_64-linux/lib  -Wl,-rpath,/usr/local/cuda-12.2/targets/x86_64-linux/lib: /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudart.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/stubs/libcuda.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublas.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcurand.so /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/x86_64-linux-gnu/libopencv_stitching.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_alphamat.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_aruco.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_barcode.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_bgsegm.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_bioinspired.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ccalib.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn_objdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn_superres.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dpm.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_face.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_freetype.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_fuzzy.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_hdf.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_hfs.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_img_hash.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_intensity_transform.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_line_descriptor.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_mcc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_quality.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_rapid.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_reg.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_rgbd.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_saliency.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_shape.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_stereo.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_structured_light.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_superres.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_surface_matching.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_tracking.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_videostab.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_viz.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_wechat_qrcode.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_xobjdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_xphoto.so.4.5.4d /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublasLt.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libculibos.a /usr/lib/x86_64-linux-gnu/libopencv_highgui.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_datasets.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_plot.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_text.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ml.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_phase_unwrapping.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_optflow.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ximgproc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_video.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_videoio.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgcodecs.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_objdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_calib3d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_features2d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_flann.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_photo.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_core.so.4.5.4d /usr/lib/gcc/x86_64-linux-gnu/11/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a -lcudadevrt -lcudart_static -lrt -lpthread -ldl \n",
            "[100%] \u001b[32m\u001b[1mLinking CXX executable darknet\u001b[0m\n",
            "cd /root/src/darknet/build/src-cli && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_link_script CMakeFiles/darknetcli.dir/link.txt --verbose=1\n",
            "/usr/bin/c++ -O3 -DNDEBUG -flto=auto -fno-fat-lto-objects -Wl,--export-dynamic -rdynamic CMakeFiles/darknetcli.dir/darknet.cpp.o \"../src-lib/CMakeFiles/darknetobjlib.dir/Chart.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/Timing.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/activations.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/art.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/blas.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/box.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/captcha.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/cifar.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/classifier.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/coco.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/col2im.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/compare.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/data.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/demo.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/detector.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dice.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/gemm.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/go.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/http_stream.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/im2col.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/image.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/list.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/local_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/matrix.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/network.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/network_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/nightmare.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/option_list.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/parser.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/region_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/rnn.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/route_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/super.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/tag.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/tree.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/utils.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/voxel.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/writing.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/yolo.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o\" -o darknet   -L/usr/local/cuda/targets/x86_64-linux/lib  -Wl,-rpath,/usr/local/cuda-12.2/targets/x86_64-linux/lib: /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudart.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/stubs/libcuda.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublas.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcurand.so /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/x86_64-linux-gnu/libopencv_stitching.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_alphamat.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_aruco.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_barcode.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_bgsegm.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_bioinspired.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ccalib.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn_objdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn_superres.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dpm.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_face.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_freetype.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_fuzzy.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_hdf.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_hfs.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_img_hash.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_intensity_transform.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_line_descriptor.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_mcc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_quality.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_rapid.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_reg.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_rgbd.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_saliency.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_shape.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_stereo.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_structured_light.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_superres.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_surface_matching.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_tracking.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_videostab.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_viz.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_wechat_qrcode.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_xobjdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_xphoto.so.4.5.4d /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublasLt.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libculibos.a /usr/lib/x86_64-linux-gnu/libopencv_highgui.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_datasets.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_plot.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_text.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ml.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_phase_unwrapping.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_optflow.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ximgproc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_video.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_videoio.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgcodecs.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_objdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_calib3d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_features2d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_flann.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_photo.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_core.so.4.5.4d /usr/lib/gcc/x86_64-linux-gnu/11/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a -lcudadevrt -lcudart_static -lrt -lpthread -ldl \n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:454:54:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Walloc-size-larger-than=\u0007-Walloc-size-larger-than=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  454 |                 classes_multipliers = (float *)calloc(classes_counters, sizeof(float));\n",
            "      |                                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/usr/include/stdlib.h:543:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
            "  543 | extern void *calloc (size_t __nmemb, size_t __size)\n",
            "      |              \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:454:54:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Walloc-size-larger-than=\u0007-Walloc-size-larger-than=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  454 |                 classes_multipliers = (float *)calloc(classes_counters, sizeof(float));\n",
            "      |                                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/usr/include/stdlib.h:543:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
            "  543 | extern void *calloc (size_t __nmemb, size_t __size)\n",
            "      |              \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[100%] Built target darknet\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[100%] Built target darknetcli\n",
            "make[1]: Leaving directory '/root/src/darknet/build'\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_progress_start /root/src/darknet/build/CMakeFiles 0\n",
            "make  -f CMakeFiles/Makefile2 preinstall\n",
            "make[1]: Entering directory '/root/src/darknet/build'\n",
            "make[1]: Nothing to be done for 'preinstall'.\n",
            "make[1]: Leaving directory '/root/src/darknet/build'\n",
            "\u001b[36mRun CPack packaging tool...\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cpack --config ./CPackConfig.cmake\n",
            "CPack: Create package using DEB\n",
            "CPack: Install projects\n",
            "CPack: - Run preinstall target for: Darknet\n",
            "CPack: - Install project: Darknet []\n",
            "CPack: Create package\n",
            "CPackDeb: - Generating dependency list\n",
            "CPack: - package: /root/src/darknet/build/darknet-2.0.225-Linux.deb generated.\n",
            "Selecting previously unselected package darknet.\n",
            "(Reading database ... 121918 files and directories currently installed.)\n",
            "Preparing to unpack darknet-2.0.225-Linux.deb ...\n",
            "Unpacking darknet (2.0.225) ...\n",
            "Setting up darknet (2.0.225) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/root\n",
            "Darknet \u001b[1;37mv2.0-225-g277ed9f4\u001b[0m\n",
            "CUDA runtime version 12020 (\u001b[1;37mv12.2\u001b[0m), driver version 12020 (\u001b[1;37mv12.2\u001b[0m)\n",
            "cuDNN version 12020 (\u001b[1;37mv8.9.6\u001b[0m), use of half-size floats is \u001b[1;37mENABLED\u001b[0m\n",
            "=> 0: \u001b[1;32mTesla T4\u001b[0m [#7.5], \u001b[1;33m14.7 GiB\u001b[0m\n",
            "OpenCV \u001b[1;37mv4.5.4\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "# make sure the notebook is set to T4 GPU (edit->settings)\n",
        "!sudo apt-get install build-essential git libopencv-dev cmake\n",
        "\n",
        "%mkdir -p ~/src\n",
        "%cd ~/src\n",
        "%rm -rf ~/src/darknet\n",
        "!git clone https://github.com/hank-ai/darknet\n",
        "%mkdir ~/src/darknet/build\n",
        "%cd ~/src/darknet/build\n",
        "!cmake -DCMAKE_BUILD_TYPE=Release ..\n",
        "!make -j $(nproc) package\n",
        "!sudo dpkg -i darknet-3*.deb\n",
        "%cd ~\n",
        "!darknet version"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# build DarkHelp"
      ],
      "metadata": {
        "id": "HeAGWqnO3EYh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build and install the DarkHelp CLI\n",
        "\n",
        "!sudo apt-get install build-essential libtclap-dev libmagic-dev libopencv-dev\n",
        "%cd ~/src\n",
        "%rm -rf ~/src/DarkHelp\n",
        "!git clone https://github.com/stephanecharette/DarkHelp.git\n",
        "%cd ~/src/DarkHelp\n",
        "%mkdir build\n",
        "%cd build\n",
        "!cmake -DCMAKE_BUILD_TYPE=Release ..\n",
        "!make -j $(nproc) package\n",
        "!sudo dpkg -i darkhelp*.deb\n",
        "%cd ~\n",
        "!DarkHelp --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rkve8tJymxLe",
        "outputId": "0afe3145-a06f-4c46-8e41-0a77c1bbc523"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "build-essential is already the newest version (12.9ubuntu3).\n",
            "libopencv-dev is already the newest version (4.5.4+dfsg-9ubuntu4+jammy0).\n",
            "The following NEW packages will be installed:\n",
            "  libmagic-dev libtclap-dev\n",
            "0 upgraded, 2 newly installed, 0 to remove and 45 not upgraded.\n",
            "Need to get 2,683 kB of archives.\n",
            "After this operation, 15.3 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libmagic-dev amd64 1:5.41-3ubuntu0.1 [105 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libtclap-dev amd64 1.2.5-1 [2,578 kB]\n",
            "Fetched 2,683 kB in 1s (3,642 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 2.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libmagic-dev:amd64.\n",
            "(Reading database ... 122013 files and directories currently installed.)\n",
            "Preparing to unpack .../libmagic-dev_1%3a5.41-3ubuntu0.1_amd64.deb ...\n",
            "Unpacking libmagic-dev:amd64 (1:5.41-3ubuntu0.1) ...\n",
            "Selecting previously unselected package libtclap-dev.\n",
            "Preparing to unpack .../libtclap-dev_1.2.5-1_amd64.deb ...\n",
            "Unpacking libtclap-dev (1.2.5-1) ...\n",
            "Setting up libmagic-dev:amd64 (1:5.41-3ubuntu0.1) ...\n",
            "Setting up libtclap-dev (1.2.5-1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "/root/src\n",
            "Cloning into 'DarkHelp'...\n",
            "remote: Enumerating objects: 1901, done.\u001b[K\n",
            "remote: Counting objects: 100% (403/403), done.\u001b[K\n",
            "remote: Compressing objects: 100% (266/266), done.\u001b[K\n",
            "remote: Total 1901 (delta 185), reused 177 (delta 119), pack-reused 1498\u001b[K\n",
            "Receiving objects: 100% (1901/1901), 10.06 MiB | 39.78 MiB/s, done.\n",
            "Resolving deltas: 100% (1206/1206), done.\n",
            "/root/src/DarkHelp\n",
            "/root/src/DarkHelp/build\n",
            "-- The C compiler identification is GNU 11.4.0\n",
            "-- The CXX compiler identification is GNU 11.4.0\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Check for working C compiler: /usr/bin/cc - skipped\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "\u001b[0mBuilding ver: 1.8.7-1\u001b[0m\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "-- Found Threads: TRUE  \n",
            "-- Found OpenCV: /usr (found version \"4.5.4\") \n",
            "-- Configuring done (0.5s)\n",
            "-- Generating done (0.1s)\n",
            "-- Build files have been written to: /root/src/DarkHelp/build\n",
            "[  2%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpConfig.cpp.o\u001b[0m\n",
            "[  4%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpNN.cpp.o\u001b[0m\n",
            "[  6%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpPositionTracker.cpp.o\u001b[0m\n",
            "[  9%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpPredictionResult.cpp.o\u001b[0m\n",
            "[ 11%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpThreads.cpp.o\u001b[0m\n",
            "[ 13%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpUtils.cpp.o\u001b[0m\n",
            "[ 16%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelp_C_API.cpp.o\u001b[0m\n",
            "[ 18%] \u001b[32m\u001b[1mLinking CXX shared library libdarkhelp.so\u001b[0m\n",
            "[ 18%] Built target dh\n",
            "[ 23%] \u001b[32mBuilding CXX object src-tool/CMakeFiles/server.dir/DarkHelpServer.cpp.o\u001b[0m\n",
            "[ 23%] \u001b[32mBuilding CXX object src-tool/CMakeFiles/cli.dir/DarkHelpCli.cpp.o\u001b[0m\n",
            "[ 25%] \u001b[32m\u001b[1mLinking CXX executable DarkHelpServer\u001b[0m\n",
            "[ 25%] Built target server\n",
            "[ 27%] \u001b[32mBuilding CXX object src-tool/CMakeFiles/combine.dir/DarkHelpCombine.cpp.o\u001b[0m\n",
            "[ 30%] \u001b[32m\u001b[1mLinking CXX executable DarkHelp\u001b[0m\n",
            "[ 30%] Built target cli\n",
            "[ 32%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_single_image.dir/display_single_image.cpp.o\u001b[0m\n",
            "[ 34%] \u001b[32m\u001b[1mLinking CXX executable DarkHelpCombine\u001b[0m\n",
            "[ 34%] Built target combine\n",
            "[ 37%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_single_image_custom_settings.dir/display_single_image_custom_settings.cpp.o\u001b[0m\n",
            "[ 39%] \u001b[32m\u001b[1mLinking CXX executable display_single_image\u001b[0m\n",
            "[ 39%] Built target display_single_image\n",
            "[ 41%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_single_image_snapping.dir/display_single_image_snapping.cpp.o\u001b[0m\n",
            "[ 44%] \u001b[32m\u001b[1mLinking CXX executable display_single_image_custom_settings\u001b[0m\n",
            "[ 44%] Built target display_single_image_custom_settings\n",
            "[ 46%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_using_bundle.dir/display_using_bundle.cpp.o\u001b[0m\n",
            "[ 48%] \u001b[32m\u001b[1mLinking CXX executable display_single_image_snapping\u001b[0m\n",
            "[ 48%] Built target display_single_image_snapping\n",
            "[ 51%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_many_images_on_threads.dir/process_many_images_on_threads.cpp.o\u001b[0m\n",
            "[ 53%] \u001b[32m\u001b[1mLinking CXX executable display_using_bundle\u001b[0m\n",
            "[ 53%] Built target display_using_bundle\n",
            "[ 55%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_single_image.dir/process_single_image.cpp.o\u001b[0m\n",
            "[ 58%] \u001b[32m\u001b[1mLinking CXX executable process_many_images_on_threads\u001b[0m\n",
            "[ 58%] Built target process_many_images_on_threads\n",
            "[ 60%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_using_bundle_and_dhthreads.dir/process_using_bundle_and_dhthreads.cpp.o\u001b[0m\n",
            "[ 62%] \u001b[32m\u001b[1mLinking CXX executable process_single_image\u001b[0m\n",
            "[ 62%] Built target process_single_image\n",
            "[ 65%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_video_webcam.dir/process_video_webcam.cpp.o\u001b[0m\n",
            "[ 67%] \u001b[32m\u001b[1mLinking CXX executable process_using_bundle_and_dhthreads\u001b[0m\n",
            "[ 67%] Built target process_using_bundle_and_dhthreads\n",
            "[ 69%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/rotate_images.dir/rotate_images.cpp.o\u001b[0m\n",
            "[ 72%] \u001b[32m\u001b[1mLinking CXX executable process_video_webcam\u001b[0m\n",
            "[ 72%] Built target process_video_webcam\n",
            "[ 74%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/save_webcam_to_video.dir/save_webcam_to_video.cpp.o\u001b[0m\n",
            "[ 76%] \u001b[32m\u001b[1mLinking CXX executable rotate_images\u001b[0m\n",
            "[ 76%] Built target rotate_images\n",
            "[ 79%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/using_c_api.dir/using_c_api.cpp.o\u001b[0m\n",
            "[ 81%] \u001b[32m\u001b[1mLinking CXX executable save_webcam_to_video\u001b[0m\n",
            "[ 81%] Built target save_webcam_to_video\n",
            "[ 83%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/video_display_realtime.dir/video_display_realtime.cpp.o\u001b[0m\n",
            "[ 86%] \u001b[32m\u001b[1mLinking CXX executable using_c_api\u001b[0m\n",
            "[ 86%] Built target using_c_api\n",
            "[ 88%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/video_object_counter.dir/video_object_counter.cpp.o\u001b[0m\n",
            "[ 90%] \u001b[32m\u001b[1mLinking CXX executable video_display_realtime\u001b[0m\n",
            "[ 90%] Built target video_display_realtime\n",
            "[ 93%] \u001b[32mBuilding CXX object src-cam/CMakeFiles/DarkHelp_cam.dir/Cam.cpp.o\u001b[0m\n",
            "[ 95%] \u001b[32m\u001b[1mLinking CXX executable video_object_counter\u001b[0m\n",
            "[ 95%] Built target video_object_counter\n",
            "[ 97%] \u001b[32mBuilding CXX object src-cam/CMakeFiles/DarkHelp_cam.dir/CamOptions.cpp.o\u001b[0m\n",
            "[100%] \u001b[32m\u001b[1mLinking CXX executable DarkHelp_cam\u001b[0m\n",
            "[100%] Built target DarkHelp_cam\n",
            "\u001b[36mRun CPack packaging tool...\u001b[0m\n",
            "CPack: Create package using DEB\n",
            "CPack: Install projects\n",
            "CPack: - Run preinstall target for: DarkHelp\n",
            "CPack: - Install project: DarkHelp []\n",
            "CPack: Create package\n",
            "CPackDeb: - Generating dependency list\n",
            "CPack: - package: /root/src/DarkHelp/build/darkhelp-1.8.7-1-Linux-x86_64-Ubuntu-22.04.deb generated.\n",
            "Selecting previously unselected package darkhelp.\n",
            "(Reading database ... 123703 files and directories currently installed.)\n",
            "Preparing to unpack darkhelp-1.8.7-1-Linux-x86_64-Ubuntu-22.04.deb ...\n",
            "Unpacking darkhelp (1.8.7-1) ...\n",
            "Setting up darkhelp (1.8.7-1) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/root\n",
            "\n",
            "DarkHelp  version: 1.8.7-1\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# using the DarkHelp Python API"
      ],
      "metadata": {
        "id": "RFTsLtbV5Q-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%mkdir -p ~/testing\n",
        "%cd ~/testing\n",
        "\n",
        "# Download the MSCOCO pre-trained weights (https://github.com/hank-ai/darknet#mscoco-pre-trained-weights).\n",
        "!wget --no-clobber https://github.com/hank-ai/darknet/releases/download/v2.0/yolov4-tiny.weights\n",
        "\n",
        "# Copy the files we're going to use.\n",
        "!cp /opt/darknet/cfg/yolov4-tiny.cfg .\n",
        "!cp /opt/darknet/cfg/coco.names .\n",
        "!cp ~/src/DarkHelp/src-python/DarkHelp.py .\n",
        "!cp ~/src/darknet/artwork/dog.jpg .\n",
        "\n",
        "import DarkHelp\n",
        "import json\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "print(\"DarkHelp v\" + DarkHelp.DarkHelpVersion().decode())\n",
        "print(\"Darknet v\" + DarkHelp.DarknetVersion().decode())\n",
        "\n",
        "# Note the order in which the filenames are specified when constructing a\n",
        "# DarkHelp object does not matter.  It will figure out which file is which.\n",
        "dh = DarkHelp.CreateDarkHelpNN(\n",
        "    \"yolov4-tiny.cfg\".encode(),\n",
        "    \"yolov4-tiny.weights\".encode(),\n",
        "    \"coco.names\".encode())\n",
        "DarkHelp.EnableAnnotationAutoHideLabels(dh, False)\n",
        "DarkHelp.EnableNamesIncludePercentage(dh, False)\n",
        "DarkHelp.EnableSnapping(dh, False)\n",
        "DarkHelp.EnableTiles(dh, False)\n",
        "DarkHelp.SetThreshold(dh, 0.45)\n",
        "DarkHelp.SetAnnotationLineThickness(dh, 1)\n",
        "\n",
        "DarkHelp.PredictFN(dh, \"dog.jpg\".encode())\n",
        "\n",
        "# Note that cv2.imread() uses BGR, while matplotlib uses RGB, so we'll skip\n",
        "# the call to cv2.cvtColor() and use matplotlib to read the image.  If we\n",
        "# need to manipulate the image in OpenCV, we'd probably be better off using\n",
        "# cv2.imread() instead.\n",
        "DarkHelp.Annotate(dh, \"output.jpg\".encode())\n",
        "img = plt.imread(\"output.jpg\")\n",
        "plt.imshow(img)\n",
        "\n",
        "# Iterate over all predictions made and display some information on each one.\n",
        "j = json.loads(DarkHelp.GetPredictionResults(dh))\n",
        "for prediction in j['file'][0]['prediction']:\n",
        "    if False:\n",
        "        # the next line displays a lot of information so turn it off by default\n",
        "        print(prediction)\n",
        "    idx = prediction['best_class']\n",
        "    name = prediction['name']\n",
        "    prob = prediction['best_probability'] * 100.0\n",
        "    x = prediction['rect']['x']\n",
        "    y = prediction['rect']['y']\n",
        "    w = prediction['rect']['width']\n",
        "    h = prediction['rect']['height']\n",
        "    print(f\"class #{idx}: {prob:.1f}% \\\"{name}\\\" at {prediction['rect']}\")\n",
        "\n",
        "    # If you want, the RoI can be extracted and you could then do whatever you\n",
        "    # want with it, such as saving it to a file or passing it to a function.\n",
        "    if False:\n",
        "        roi = img[y:y+h, x:x+w]\n",
        "        plt.imshow(roi)\n",
        "\n",
        "DarkHelp.DestroyDarkHelpNN(dh)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "X0jhrVK75cWB",
        "outputId": "d45828b5-622a-4240-90c4-8c1066bb82ed"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/testing\n",
            "--2024-06-07 14:23:28--  https://github.com/hank-ai/darknet/releases/download/v2.0/yolov4-tiny.weights\n",
            "Resolving github.com (github.com)... 140.82.116.3\n",
            "Connecting to github.com (github.com)|140.82.116.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/642508441/5c526108-47f1-4e75-a7a7-6670ba59aac9?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240607%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240607T142328Z&X-Amz-Expires=300&X-Amz-Signature=619ffd515ae0cf0ec364a3826b19ad14cfc4e1d4056bdc02b42e05da974940ae&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=642508441&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.weights&response-content-type=application%2Foctet-stream [following]\n",
            "--2024-06-07 14:23:28--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/642508441/5c526108-47f1-4e75-a7a7-6670ba59aac9?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240607%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240607T142328Z&X-Amz-Expires=300&X-Amz-Signature=619ffd515ae0cf0ec364a3826b19ad14cfc4e1d4056bdc02b42e05da974940ae&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=642508441&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.weights&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 24251276 (23M) [application/octet-stream]\n",
            "Saving to: ‘yolov4-tiny.weights’\n",
            "\n",
            "yolov4-tiny.weights 100%[===================>]  23.13M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2024-06-07 14:23:29 (189 MB/s) - ‘yolov4-tiny.weights’ saved [24251276/24251276]\n",
            "\n",
            "DarkHelp v1.8.7-1\n",
            "Darknet v2.0.225\n",
            "class #1: 60.5% \"bicycle\" at {'height': 379, 'width': 505, 'x': 72, 'y': 100}\n",
            "class #7: 81.5% \"truck\" at {'height': 91, 'width': 240, 'x': 464, 'y': 80}\n",
            "class #16: 87.0% \"dog\" at {'height': 336, 'width': 182, 'x': 137, 'y': 206}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}
